The OpenAIRE Research Community Dashboard: On blending scientific workflows and scientific publishing

First Online 30 August 2019Despite the hype, the effective implementation of Open Science is hindered by several cultural and technical barriers. Researchers embraced digital science, use “digital laboratories” (e.g. research infrastructures, thematic services) to conduct their research and publish research data, but practices and tools are still far from achieving the expectations of transparency and reproducibility of Open Science. The places where science is performed and the places where science is published are still regarded as different realms. Publishing is still a post-experimental, tedious, manual process, too often limited to articles, in some contexts semantically linked to datasets, rarely to software, generally disregarding digital representations of experiments. In this work we present the OpenAIRE Research Community Dashboard (RCD), designed to overcome some of these barriers for a given research community, minimizing the technical efforts and without renouncing any of the community services or practices. The RCD flanks digital laboratories of research communities with scholarly communication tools for discovering and publishing interlinked scientific products such as literature, datasets, and software. The benefits of the RCD are show-cased by means of two real-case scenarios: the European Marine Science community and the European Plate Observing System (EPOS) research infrastructure.This work is partly funded by the OpenAIRE-Advance H2020 project (grant number: 777541; call: H2020-EINFRA-2017) and the OpenAIREConnect H2020 project (grant number: 731011; call: H2020-EINFRA-2016-1). Moreover, we would like to thank our colleagues Michele Manunta, Francesco Casu, and Claudio De Luca (Institute for the Electromagnetic Sensing of the Environment, CNR, Italy) for their work on the EPOS infrastructure RCD; and Stephane Pesant (University of Bremen, Germany) his work on the European Marine Science RCD

Dynamic Optimization for Vortex Shedding Suppression

Flows around structures exhibiting vortex shedding induce vibrations that can potentially damage the structure. A way to avoid it is to suppress vortex shedding by controlling the wake. Wake control of laminar flow behind a rotating cylinder is formulated herein as a dynamic optimization problem. Angular cylinder speed is the manipulated variable that is adjusted to suppress vortex shedding by minimizing lift coefficient variation. The optimal angular speed is assumed to be periodic like wake formation. The control problem is solved for different time horizons tH. The impact of tH to control is evaluated and the need for feedback is assessed

Linear MPC based on data-driven artificial neural networks for large-scale nonlinear distributed parameter systems

Process controller synthesis with detailed models is a challenging task, which may lead to many advantageous closed-loop features. Model reduction such as Proper Orthogonal Decomposition (POD) and (adaptive) linearization can be applied to tackle with the arising problems, whereas process data can be directly used to build accurate models via training of artificial neural networks (ANN). In this contribution, we present two methodologies we have recently developed, which combine ANN with POD, for use in the context of MPC: the process at hand is represented as a sum of products of time- varying coefficients (computed with ANN) with the POD basis functions computed from plant “snapshots”. The resulting accurate model can be used in NMPC, or trajectory piecewise linearization along a reference path can be applied on the ANN, yielding a series of linear models, suitable for linear MPC